easy_dwpose/README.md

fast_dwpose

The goal of Easy DWPose is to provide a generic, reliable, and easy-to-use interface for making skeletons for ControlNet.

SF do some improve for easy-dwpose, named it fast-dwpose.

Installation

PIP

pip install easy-dwpose

Quickstart

In you own .py scrip or in Jupyter

import torch
from PIL import Image
import numpy as np
import json

from easy_dwpose import DWposeDetector

#####---------Setup init
# You can use a different GPU, e.g. "cuda:1"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
detector = DWposeDetector(device=device)
input_image = Image.open("assets/pose.png").convert("RGB")


#####---------Get both the skeleton image
# SF: skeleton should be a kind of img 
skeleton = detector(input_image, output_type="pil", include_hands=True, include_face=True)
# Save the skeleton image
skeleton.save("skeleton.png")


#####---------Get pose data
# SF: pose_data should be numpy/tensor
# # This returns the dictionary
pose_data = detector(input_image, draw_pose=False)

# Save the skeleton pose information:
# Option 1: Save as NPY file
np.save('pose_data.npy', pose_data)

# Option 2: Save as NPZ file
np.savez('pose_data.npz', **pose_data)

# Option 3: Save as JSON file
# Convert numpy arrays to lists for JSON serialization
pose_data_json = {k: v.tolist() if isinstance(v, np.ndarray) else v for k, v in pose_data.items()}
with open('pose_data.json', 'w') as f:
    json.dump(pose_data_json, f)

Input	Output

On a video

python scripts/inference_on_video.py --input assets/dance.mp4 --output_path result.mp4

Input	Output

On a folder of images

python scripts/inference_on_folder.py --input assets/ --output_path results/

Easy-DWPose Custom skeleton drawing

By default, we use standart skeleton drawing function but several projects change it (e.g. MusePose). Modify it or write your own from scratch!

from PIL import Image
from easy_dwpose import DWposeDetector
from easy_dwpose.draw.musepose import draw_pose as draw_pose_musepose

detector = DWposeDetector(device="cpu")
input_image = Image.open("assets/pose.png").convert("RGB")

skeleton = detector(input_image, output_type="pil", draw_pose=draw_pose_musepose, draw_face=False)
skeleton.save("skeleton.png")

SF Custom skeleton drawing

I prefer ControlNext style, I have developed a visualization method and placed it in ./easy_dwpose/draw/controlnext.py. It does not integrate with easy dwpose and the calling method is slightly different:

import torch
from PIL import Image
import numpy as np
import json

from easy_dwpose import DWposeDetector
from easy_dwpose.draw.controlnext import draw_pose, process_pose_data 

#####---------Setup init
# You can use a different GPU, e.g. "cuda:1"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
detector = DWposeDetector(device=device)
input_image = Image.open("assets/pose.png").convert("RGB")

#####---------Custom ControlNext drawing style
# Get pose data for custom drawing
pose_data = detector(input_image, draw_pose=False)

# Get image dimensions
width, height = input_image.size

# Process the pose data for custom drawing
processed_pred = process_pose_data(pose_data, height, width)

# Draw pose using custom ControlNext style
vis_img = draw_pose(
    pose=processed_pred,
    H=height,
    W=width,
    include_body=True,
    include_hand=True,
    include_face=True
)

# Convert to PIL Image and save (vis_img is in CHW format)
custom_skeleton = Image.fromarray(vis_img.transpose(1, 2, 0))
custom_skeleton.save("skeleton_controlnext.png")

Acknowledgement

We thank the original authors of the DWPose for their incredible models!

Thanks for open-sourcing!